question_first experimentrun1_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run1_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.3535356 0.0739461 -31.827717 2.677800e-222
## 2 mask_c 0.1141501 0.1002212 1.138981 2.547110e-01
## 3 feat_c 0.3238524 0.1002257 3.231232 1.232577e-03
## 4 mask_c:feat_c 0.4674783 0.2004747 2.331856 1.970826e-02
run2_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run2_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.58289322 0.07711169 -33.4954800 5.608313e-246
## 2 mask_c 0.07250391 0.10333360 0.7016490 4.828981e-01
## 3 feat_c 0.35802662 0.10333820 3.4646106 5.309997e-04
## 4 mask_c:feat_c -0.19824320 0.20666460 -0.9592509 3.374324e-01
run3_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run3_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.620989644 0.07867521 -33.31404806 2.417198e-243
## 2 mask_c -0.004390499 0.08480725 -0.05177033 9.587117e-01
## 3 feat_c 0.295993160 0.08481014 3.49006801 4.828976e-04
## 4 mask_c:feat_c 0.019228611 0.16966914 0.11333004 9.097689e-01
feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = question_first)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.53963192 0.04792108 -52.9961277 0.000000e+00
## 2 mask_c 0.05179057 0.05472818 0.9463236 3.439836e-01
## 3 feat_c 0.32306541 0.05473370 5.9024956 3.580437e-09
## 4 mask_c:feat_c 0.09328981 0.10946050 0.8522692 3.940647e-01